Summary of Machine Learning-based Completions Sequencing For Well Performance Optimization, by Anjie Liu et al.
Machine Learning-Based Completions Sequencing for Well Performance Optimization
by Anjie Liu, Jinglang W. Sun, Anh Ngo, Ademide O. Mabadeje, Jose L. Hernandez-Mejia
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a machine learning approach to optimize long-term oil production by developing effective models that integrate the effects of multidimensional predictive variables. Traditional simulation software is computationally expensive, making it difficult for oil and gas companies to forecast production efficiently. The authors aim to create machine-learning models that can accurately predict 12-Month Cumulative Production by considering various completion conditions. This study contributes to the development of efficient methods for optimizing well developments, which is crucial in the oil and gas industry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to help oil companies make better decisions about how to get more oil out of their wells. For a long time, they’ve used special computer programs that take a lot of time and effort to run. But now, scientists are using computers to learn from data and make good guesses about what will happen in the future. The goal is to create a smart system that can look at lots of different factors, like how much oil is flowing out of the well, to predict how much oil they’ll get over time. |
Keywords
* Artificial intelligence * Machine learning
